CN116798636A

CN116798636A - Medical diagnostic method and related apparatus

Info

Publication number: CN116798636A
Application number: CN202210247752.2A
Authority: CN
Inventors: 郑超; 肖月庭; 阳光
Original assignee: Shukun Beijing Network Technology Co Ltd
Current assignee: Shukun Beijing Network Technology Co Ltd
Priority date: 2022-03-14
Filing date: 2022-03-14
Publication date: 2023-09-22
Anticipated expiration: 2042-03-14
Also published as: CN116798636B

Abstract

The application discloses a medical diagnosis method and related equipment, which can acquire target medical detection data aiming at a target physiological part of an object to be diagnosed; performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part; acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part; and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result. The application can comprehensively diagnose the object to be diagnosed by combining the related physiological information with the common relation with the target physiological position, thereby improving the accuracy of the diagnosis result.

Description

Medical diagnostic method and related apparatus

Technical Field

The application relates to the technical field of computers, in particular to a medical diagnosis method and related equipment.

Background

In the diagnosis process of doctors, only a certain local area (a part, an organ, a tissue or the like) or a certain system (a nervous system, a blood system and the like) of a to-be-diagnosed object is generally diagnosed at present, so that a diagnosis conclusion and treatment suggestions are given according to the pathological changes of the local area. In the whole diagnosis and treatment process, doctors and patients do not establish complete information integration and knowledge on the whole body condition of the patients. Such a diagnosis method easily causes erroneous judgment or missed diagnosis, and the accuracy of the obtained diagnosis result is low.

Disclosure of Invention

The embodiment of the application provides a medical diagnosis method and related equipment, wherein the related equipment can comprise a medical diagnosis device, electronic equipment, a computer readable storage medium and a computer program product, can comprehensively diagnose an object to be diagnosed, and improves the accuracy of a diagnosis result.

The embodiment of the application provides a medical diagnosis method, which comprises the following steps:

acquiring target medical detection data for a target physiological site of a subject to be diagnosed;

performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part;

acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part;

and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result.

Accordingly, an embodiment of the present application provides a medical diagnostic apparatus including:

a first acquisition unit configured to acquire target medical detection data for a target physiological site of a subject to be diagnosed;

The identification unit is used for identifying the focus of the target medical detection data to obtain focus attribute information corresponding to the target physiological part;

a second acquisition unit, configured to acquire a commonality analysis rule corresponding to focus attribute information of the target physiological site, where the commonality analysis rule indicates associated physiological information having a commonality relationship with the target physiological site;

and the diagnosis unit is used for carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result.

Optionally, in some embodiments of the application, the associated physiological information includes associated physiological site information having a commonality with the target physiological site; the diagnosis unit may specifically be configured to obtain medical detection data for the associated physiological site information of the object to be diagnosed based on the commonality analysis rule; and carrying out disease deduction analysis on the object to be diagnosed based on the medical detection data of the associated physiological position information and the focus attribute information to obtain a target diagnosis result.

Optionally, in some embodiments of the present application, the associated physiological information includes associated physiological index information having a commonality relationship with the target physiological site; the diagnosis unit may specifically be configured to obtain medical detection data for the associated physiological index information of the object to be diagnosed based on the commonality analysis rule; and carrying out disease deduction analysis on the object to be diagnosed based on the medical detection data of the associated physiological index information and the focus attribute information to obtain a target diagnosis result.

Optionally, in some embodiments of the present application, the medical diagnostic apparatus may further include a reference diagnostic unit for retrieving reference medical detection data of the object to be diagnosed under a target period of time when a commonality analysis rule corresponding to the lesion attribute information is not acquired; determining similar medical detection data matched with the target medical detection data from the reference medical detection data; and carrying out disease deduction analysis on the object to be diagnosed according to the focus attribute information and the similar medical detection data to obtain a target diagnosis result.

Optionally, in some embodiments of the present application, the second obtaining unit may include a first obtaining subunit and a querying subunit, as follows:

the first acquisition subunit is configured to acquire a preset commonality relation set, where the preset commonality relation set includes a mapping relation between focus attribute information of a preset physiological part and a preset commonality analysis rule;

and the inquiring subunit is used for inquiring the commonality analysis rule corresponding to the focus attribute information of the target physiological part from the preset commonality relation set.

Alternatively, in some embodiments of the present application, the diagnostic unit may include a second acquisition subunit and a diagnostic subunit, as follows:

The second acquisition subunit is used for acquiring associated focus attribute information corresponding to the medical detection data of the associated physiological information;

and the diagnosis subunit is used for diagnosing the object to be diagnosed based on diagnosis experience related information corresponding to the preset association condition when the association relation between the association focus attribute information and the focus attribute information meets the preset association condition, so as to obtain a target diagnosis result.

Optionally, in some embodiments of the present application, the medical diagnosis apparatus may further include a case diagnosis unit, where the case diagnosis unit is configured to select, from candidate cases, similar cases matching with the diagnostic process of the object to be diagnosed when the degree of difference between the diagnostic experience-related information corresponding to the preset association condition and the diagnostic process corresponding to the object to be diagnosed is greater than a preset value; and determining a target diagnosis result of the object to be diagnosed based on the diagnosis result of the similar case.

Optionally, in some embodiments of the present application, the identifying unit may be specifically configured to perform focus identification on the target medical detection data through a trained focus identification model, so as to obtain focus attribute information corresponding to the target physiological location.

Optionally, in some embodiments of the present application, the medical diagnostic apparatus may further include a training unit for training a lesion recognition model; specifically, the training unit may be configured to obtain training data, where the training data includes expected focus attribute information corresponding to a sample physiological site and sample medical detection data corresponding to the sample physiological site; performing focus identification on the sample medical detection data through a focus identification model to obtain actual focus attribute information corresponding to the sample physiological part; and adjusting parameters of the focus recognition model based on the expected focus attribute information and the actual focus attribute information to obtain a trained focus recognition model.

The electronic device provided by the embodiment of the application comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor loads the instructions to execute the steps in the medical diagnosis method provided by the embodiment of the application.

The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps in the medical diagnosis method provided by the embodiment of the application.

In addition, the embodiment of the application also provides a computer program product, which comprises a computer program or instructions, and the computer program or instructions realize the steps in the medical diagnosis method provided by the embodiment of the application when being executed by a processor.

The embodiment of the application provides a medical diagnosis method and related equipment, which can acquire target medical detection data aiming at a target physiological part of a to-be-diagnosed object; performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part; acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part; and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result. The application can comprehensively diagnose the object to be diagnosed by combining the related physiological information with the common relation with the target physiological position, thereby improving the accuracy of the diagnosis result.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1a is a schematic view of a medical diagnostic method provided by an embodiment of the present application;

FIG. 1b is a flow chart of a medical diagnostic method provided by an embodiment of the present application;

FIG. 2 is another flow chart of a medical diagnostic method provided by an embodiment of the present application;

FIG. 3 is a schematic view of a medical diagnostic apparatus according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

Embodiments of the present application provide a medical diagnostic method and related apparatus, which may include medical diagnostic devices, electronic equipment, computer-readable storage media, and computer program products. The medical diagnostic apparatus may be integrated in an electronic device, which may be a terminal or a server or the like.

It will be appreciated that the medical diagnostic method of the present embodiment may be performed on the terminal, may be performed on the server, or may be performed by both the terminal and the server. The above examples should not be construed as limiting the application.

As shown in fig. 1a, an example is a method in which a terminal and a server perform medical diagnosis together. The medical diagnosis system provided by the embodiment of the application comprises a terminal 10, a server 11 and the like; the terminal 10 and the server 11 are connected via a network, for example, a wired or wireless network connection, etc., wherein the medical diagnostic apparatus may be integrated in the server.

Wherein the server 11 may be used for acquiring target medical detection data for a target physiological site of a subject to be diagnosed; performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part; acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part; and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result, and sending the target diagnosis result to the terminal 10. The server 11 may be a single server, or may be a server cluster or cloud server composed of a plurality of servers.

Wherein, terminal 10 can be used for: the target medical detection data of the target physiological site of the object to be diagnosed is transmitted to the server 11, and the target diagnosis result transmitted by the server 11 may also be received. The terminal 10 may include a mobile phone, a tablet computer, a notebook computer, a personal computer (PC, personal Computer), or the like. A client may also be provided on the terminal 10, which may be an application client or a browser client, etc.

The above-described step of performing medical diagnosis by the server 11 may also be performed by the terminal 10.

The medical diagnosis method provided by the embodiment of the application relates to machine learning in the field of artificial intelligence.

Among these, artificial intelligence (AI, artificial Intelligence) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and expand human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision. The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning, automatic driving, intelligent traffic and other directions.

Among them, machine Learning (ML) is a multi-domain interdisciplinary, and involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.

The following will describe in detail. The following description of the embodiments is not intended to limit the preferred embodiments.

The present embodiment will be described from the viewpoint of a medical diagnosis apparatus, which may be integrated in an electronic device, which may be a server or a terminal or the like.

As shown in fig. 1b, the specific flow of the medical diagnosis method may be as follows:

101. target medical detection data for a target physiological site of a subject to be diagnosed is acquired.

The object to be diagnosed may be an object to be diagnosed for a disease, and the object may be a human or an animal, which is not limited in this embodiment.

The target physiological site may be an organ, a tissue, a system, or the like of the object to be diagnosed, such as a nervous system, a blood system, or the like, which is not limited in this embodiment.

The target medical detection data may be medical information obtained by performing medical detection on the target physiological site, and may specifically be medical image data.

For example, the target medical detection data may be a medical image obtained by scanning a physiological structure of a human body by means of an electronic computed tomography (Computed Tomography, CT), a magnetic resonance examination (Magnetic Resonance, MR), a 4D (4-dimensional) ultrasound, or the like. The physiological structure of the human body can be heart, liver, lung, blood vessel, bone, etc.

For another example, the target medical test data may also be a diagnostic report, laboratory sheet, etc. of the patient. The obtained chart data can be used as medical detection data by detecting a physiological structure or region of a human body target through an electrocardiograph device, an electroencephalograph device, a DR (Digital Radiography ) device (such as chest radiography device and the like), a DSA (Digital subtraction angiography, digital subtraction technique) device, an endoscope device and the like.

In the related art, a doctor usually diagnoses only a certain local area (site, organ or tissue, etc.) or a certain system (nervous system, blood system, etc.) of a subject to be diagnosed, so as to give a diagnosis conclusion and a treatment suggestion according to the pathological condition of the local area. In the whole diagnosis and treatment process, doctors and patients do not establish complete information integration and understanding on the whole body condition of the patients, so that the patients cannot know whether the pathological changes of a certain local area exist in the area or not, and serious pathological changes occur in the area due to the fact that other areas also exist in the area. However, many diseases may be caused by problems in a plurality of areas (sites, organs or tissues, etc.), and a serious lesion is finally caused in a certain local area. Diagnosis of a local area only often cannot find the actual cause of disease or the source of the disease, and comprehensive diagnosis and treatment suggestions are difficult to give.

According to the application, the lesion areas at a plurality of parts of the body can be comprehensively analyzed according to the relevance of the lesions among the parts of the body, and accurate and comprehensive diagnosis and treatment suggestions aiming at the target area (namely the target physiological part) are provided. Wherein the relevance of lesions between body parts is generally not a diagnostic experience that primary or secondary doctors can learn and learn, which is usually only held in the hands of some senior specialists. The application can collect diagnosis experiences with etiology association among the parts to form an automatic artificial intelligent diagnosis method, thereby assisting doctors to combine the condition changes of a plurality of physiological parts of the human body and giving comprehensive diagnosis results and diagnosis and treatment suggestions of the condition of a target area.

102. And carrying out focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological position.

The focus identification may specifically be to identify a part of the body where a lesion occurs. The lesion attribute information is specifically, i.e., a lesion characteristic, such as the occurrence of a non-calcified plaque at a certain location.

Optionally, in this embodiment, the step of performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological location may include:

and performing focus recognition on the target medical detection data through the trained focus recognition model to obtain focus attribute information corresponding to the target physiological position.

The lesion recognition model may be a neural Network model, and in particular, may be a visual geometry group Network (VGGNet, visual Geometry Group Network), a Residual Network (Residual Network), a dense connection convolutional Network (DenseNet, dense Convolutional Network), or the like, but it should be understood that the lesion recognition model of the present embodiment is not limited to only the types listed above.

The embodiment can identify the focus and the attribute information thereof in the medical detection data by using a focus identification model which is constructed in advance. The focus recognition model can be formed by taking marked focus information as input and training the focus recognition model for multiple times through a deep learning neural network or a traditional algorithm.

The lesion recognition model may be specifically trained by other devices and then provided to the medical diagnostic apparatus, or may be self-trained by the medical diagnostic apparatus.

If the medical diagnosis device trains itself, before the step of performing focus recognition on the target medical detection data through the trained focus recognition model to obtain focus attribute information corresponding to the target physiological position, the method may further include:

acquiring training data, wherein the training data comprises sample medical detection data corresponding to a sample physiological part and expected focus attribute information corresponding to the sample physiological part;

performing focus identification on the sample medical detection data through a focus identification model to obtain actual focus attribute information corresponding to the sample physiological part;

and adjusting parameters of the focus recognition model based on the expected focus attribute information and the actual focus attribute information to obtain a trained focus recognition model.

The training process comprises the steps of firstly identifying actual focus attribute information corresponding to a sample physiological part, then adjusting parameters of a focus identification model by using a back propagation algorithm, and optimizing parameters of the focus identification model based on expected focus attribute information and the actual focus attribute information, so that the actual focus attribute information approaches to the expected focus attribute information, and a trained focus identification model is obtained. Specifically, the loss value between the identified actual lesion attribute information and the corresponding expected lesion attribute information may be made smaller than a preset value, which may be set according to the actual situation.

103. And acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part.

The commonality relationship may specifically refer to an internal association relationship. Specifically, the commonality analysis rule may further include: when the associated physiological information has certain associated focus attribute information, the associated focus attribute information is focus attribute information of which the association relation with focus attribute information of the target physiological part meets the preset association condition, the object to be diagnosed possibly has indication information of a certain disease.

Optionally, in this embodiment, the step of "obtaining a commonality analysis rule corresponding to the lesion attribute information of the target physiological site" may include:

acquiring a preset commonality relation set, wherein the preset commonality relation set comprises a mapping relation between focus attribute information of a preset physiological part and a preset commonality analysis rule;

and inquiring a commonality analysis rule corresponding to the focus attribute information of the target physiological part from the preset commonality relation set.

Wherein, the preset commonality relation set can be constructed based on the diagnosis experience (which can include practical diagnosis experience and/or theoretical diagnosis experience) of some advanced doctors; specifically, the diagnostic experience of a senior physician may be: association of lesion formation between which sites exists; or which organs have a correlation between lesion formations; or which blood vessels there is a correlation of lesion formation between them.

The preset commonality relation set can be updated in real time. For example, the set of preset commonalities may be updated with follow-up information or new expert experience. Follow-up refers to: the change condition of the focus along with treatment is obtained through regular tracking and observation of patients.

Specifically, the preset commonality relation set may be a relation table of focus attribute information of a preset physiological part and a commonality analysis rule corresponding to the focus attribute information. Where commonalities may in particular mean the inherent association that exists between the two.

The associated physiological information may include associated physiological location information, associated physiological index information, and the like, which is not limited in this embodiment. The associated physiological site information may include a lesion sign associated with the physiological site, a location where the lesion appears, or a distribution pattern of the lesion, etc. Associated physiological site information may be understood as information associated with the target physiological site on a macroscopic level; the associated physiological index information is understood as information associated with the target physiological site at a microscopic level, and is a clue that can be used to analyze the etiology of the target physiological site.

The medical detection data related to the physiological information can be medical image data, laboratory sheets and the like.

In some embodiments, the associated physiological site may be the same as or different from the target physiological site, which is not limited by the present embodiment. For example, the target physiological site and the associated physiological site can be lung, the focus attribute information of the target physiological site is pneumonia focus, and the medical detection data of the associated physiological site information can be information recorded in a clinical laboratory sheet of the lung of the patient.

In this embodiment, the attribute information of the lesions at different positions may be used as evidence of the following conclusion; rules of the following conclusion are defined by the diagnosis experience of the senior expert; the diagnosis conclusion is obtained by combining the focus attribute information and the diagnosis experience of the senior expert.

104. And carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result.

Optionally, in this embodiment, the associated physiological information includes associated physiological site information having a commonality relationship with the target physiological site;

the step of performing disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result may include:

Acquiring medical detection data of the associated physiological site information for the object to be diagnosed based on the commonality analysis rule;

and carrying out disease deduction analysis on the object to be diagnosed based on the medical detection data of the associated physiological position information and the focus attribute information to obtain a target diagnosis result.

Optionally, in this embodiment, the associated physiological information includes associated physiological index information having a commonality relationship with the target physiological site;

acquiring medical detection data of the associated physiological index information of the object to be diagnosed based on the commonality analysis rule;

and carrying out disease deduction analysis on the object to be diagnosed based on the medical detection data of the associated physiological index information and the focus attribute information to obtain a target diagnosis result.

In some embodiments, medical information having a commonality relationship with the lesion attribute information of the target physiological site may be determined based on a preset commonality relationship set, and a diagnosis result may be given accordingly.

In one specific scenario, the lung lesions are exemplified, i.e., the target physiological site is the lung. A pneumonia lesion (lesion attribute information of a target physiological site) is detected in the lung DR image. In general, it is not easy to determine whether the pneumonitis is caused by viruses, bacteria or mycoplasma, and in this embodiment, a preset commonality relation set obtained by summarizing information of various commonalities by an expert may be used, and a clinical laboratory sheet of the lung of the patient may be obtained at the same time, and according to a pneumonitis focus and index information (associated physiological index information) recorded in the clinical laboratory sheet, the pneumonitis focus may be given as a pneumonitis belonging to viruses.

In other embodiments, based on a preset common relation set, medical information (specifically, associated physiological information) having an association relation with focus attribute information of the target physiological site is searched, if not, contemporaneous medical detection data of the patient can be called, at least one medical detection data similar to the target medical detection data of the target physiological site is searched, and a diagnosis result is correspondingly given. Wherein contemporaneous medical test data may refer to medical test data within a certain short time frame, e.g., within hours, days or months.

Optionally, in this embodiment, the medical diagnosis method may further include:

when the commonality analysis rule corresponding to the focus attribute information is not acquired, the reference medical detection data of the object to be diagnosed in the target time period is called;

determining similar medical detection data matched with the target medical detection data from the reference medical detection data;

and carrying out disease deduction analysis on the object to be diagnosed according to the focus attribute information and the similar medical detection data to obtain a target diagnosis result.

The target period may be set according to practical situations, which is not limited in this embodiment. Such as one month, half year, etc.

The matching degree of each piece of reference medical detection data and the target medical detection data can be determined first, and then the reference medical detection data with the matching degree meeting the preset matching condition is selected from the reference medical detection data to serve as similar medical detection data of the target medical detection data. The preset matching condition can be set according to actual conditions.

In this embodiment, the method of acquiring the contemporaneous similar medical test data is prone to integration, i.e. the causal medical test data are integrated/organized together according to causal relationships, so that a more comprehensive diagnosis is performed according to the integration result.

In a specific scenario, taking a non-calcified plaque of the brain as an example, the target physiological region is the brain, and the focus attribute information is the non-calcified plaque. Non-calcified plaque is detected in the brain CT image, and according to the information of the detected non-calcified plaque in the brain CT image, a possible condition can be primarily determined by using the preset common relation set in the above embodiment, but a conclusion cannot be directly drawn. At this time, medical detection data (i.e., medical information) having commonality can be found in the concurrent examination of the patient. During the search, the patient is found to have non-calcified plaque in the heart region, at which time a diagnosis can be made: blood has high lipid content, is easy to deposit, and needs to be treated for reducing blood fat.

Optionally, in this embodiment, the step of performing disease deduction analysis on the object to be diagnosed based on the associated physiological information and the lesion attribute information to obtain a target diagnosis result may include:

acquiring associated focus attribute information corresponding to medical detection data of the associated physiological information;

and when the association relation between the association focus attribute information and the focus attribute information meets a preset association condition, diagnosing the object to be diagnosed based on diagnosis experience related information corresponding to the preset association condition to obtain a target diagnosis result.

The preset association condition and the diagnosis experience related information corresponding to the preset association condition may be recorded in a preset common relation set, and if the preset association condition is satisfied, the corresponding diagnosis experience related information recorded in the preset common relation set may be used as a reference to diagnose the object to be diagnosed. In addition, the commonality analysis rule may also include corresponding information related to diagnosis experience, which is not limited in this embodiment.

For example, in a specific scenario, the target physiological part is a brain, the focus attribute information thereof is a non-calcified plaque, the associated physiological part is a heart, the detected associated focus attribute information thereof is also a non-calcified plaque, and the preset association condition that the heart and the brain recorded in the preset common relation set have the non-calcified plaque is met, so that corresponding diagnosis experience related information can be obtained as a reference for diagnosis.

when the difference degree of the diagnosis experience related information corresponding to the preset association condition and the diagnosis process corresponding to the object to be diagnosed is larger than a preset value, selecting a similar case matched with the diagnosis process of the object to be diagnosed from candidate cases;

And determining a target diagnosis result of the object to be diagnosed based on the diagnosis result of the similar case.

The preset value can be set according to actual conditions. The candidate cases may specifically be cases in a pre-set case database.

If a diagnosis experience which is contrary to or has a large difference with the diagnosis process of the object to be diagnosed is recorded in the commonality relation table (i.e. the preset commonality relation set), the diagnosis result of the case similar to the patient case can be searched from the preset case database according to the condition of the current case and pushed to a doctor as a diagnosis reference.

According to the application, on the basis of detecting the focus of a certain part, medical detection data of other parts with commonality relation can be determined according to a preset commonality relation set constructed in advance, and comprehensive diagnosis results and diagnosis and treatment suggestions are given by combining the pathological changes of a plurality of parts; or searching medical detection data with a commonality relation in the contemporaneous reference medical detection data according to the target medical detection data, and giving out comprehensive diagnosis results and diagnosis and treatment suggestions. The diagnosis conclusion and the treatment suggestion obtained through the commonality relation can more comprehensively show the formation cause and the association of the focus, and meanwhile, the mutual support of the focus causes among different areas can be formed, so that the reliability of diagnosis is higher.

From the above, the present embodiment can acquire target medical detection data for a target physiological site of a subject to be diagnosed; performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part; acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part; and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result. The application can comprehensively diagnose the object to be diagnosed by combining the related physiological information with the common relation with the target physiological position, thereby improving the accuracy of the diagnosis result.

The method according to the previous embodiments will be described in further detail below with the specific integration of the medical diagnostic apparatus in the terminal.

The embodiment of the application provides a medical diagnosis method, as shown in fig. 2, the specific flow of the medical diagnosis method can be as follows:

201. the terminal acquires target medical detection data for a target physiological site of a subject to be diagnosed.

202. And the terminal performs focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological position.

203. The terminal acquires a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part.

204. And the terminal performs disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result.

In some embodiments, based on a preset common relation set, medical information (specifically, associated physiological information) having an association relation with focus attribute information of a target physiological part is searched, if the medical information cannot be searched, contemporaneous medical detection data of the patient can be called, at least one medical detection data similar to the target medical detection data of the target physiological part is searched, and a diagnosis result is correspondingly given. Wherein contemporaneous medical test data may refer to medical test data within a certain short time frame, e.g., within hours, days or months.

The preset association condition and the diagnosis experience related information corresponding to the preset association condition may be recorded in a preset common relation set, and if the preset association condition is satisfied, the corresponding diagnosis experience related information recorded in the preset common relation set may be used as a reference to diagnose the object to be diagnosed.

As can be seen from the above, in this embodiment, the terminal may obtain target medical detection data for a target physiological site of a subject to be diagnosed; performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part; acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part; and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result. The application can comprehensively diagnose the object to be diagnosed by combining the related physiological information with the common relation with the target physiological position, thereby improving the accuracy of the diagnosis result.

In order to better implement the above method, the embodiment of the present application further provides a medical diagnostic apparatus, as shown in fig. 3, which may include a first acquisition unit 301, an identification unit 302, a second acquisition unit 303, and a diagnostic unit 304, as follows:

(1) A first acquisition unit 301;

a first acquisition unit for acquiring target medical detection data for a target physiological site of a subject to be diagnosed.

(2) An identification unit 302;

and the identification unit is used for identifying the focus of the target medical detection data and obtaining focus attribute information corresponding to the target physiological position.

(3) A second acquisition unit 303;

and the second acquisition unit is used for acquiring a commonality analysis rule corresponding to the focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with a commonality relation with the target physiological part.

(4) A diagnostic unit 304;

As can be seen from the above, the present embodiment can acquire target medical detection data for a target physiological site of a subject to be diagnosed by the first acquisition unit 301; the identification unit 302 performs focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological position; acquiring, by the second acquisition unit 303, a commonality analysis rule corresponding to focus attribute information of the target physiological site, the commonality analysis rule indicating associated physiological information having a commonality relationship with the target physiological site; and performing disease deduction analysis on the object to be diagnosed by using the diagnosis unit 304 based on the associated physiological information and the focus attribute information to obtain a target diagnosis result. The application can combine the associated physiological information to carry out comprehensive diagnosis on the object to be diagnosed, thereby improving the accuracy of the diagnosis result.

The embodiment of the application also provides an electronic device, as shown in fig. 4, which shows a schematic structural diagram of the electronic device according to the embodiment of the application, where the electronic device may be a terminal or a server, specifically:

the electronic device may include one or more processing cores 'processors 401, one or more computer-readable storage media's memory 402, power supply 403, and input unit 404, among other components. Those skilled in the art will appreciate that the electronic device structure shown in fig. 4 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components. Wherein:

the processor 401 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 402, and calling data stored in the memory 402. Optionally, processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, an application program, etc., and the modem processor mainly processes wireless communication. It will be appreciated that the modem processor described above may not be integrated into the processor 401.

The memory 402 may be used to store software programs and modules, and the processor 401 executes various functional applications and data processing by executing the software programs and modules stored in the memory 402. The memory 402 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the electronic device, etc. In addition, memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

The electronic device further comprises a power supply 403 for supplying power to the various components, preferably the power supply 403 may be logically connected to the processor 401 by a power management system, so that functions of managing charging, discharging, and power consumption are performed by the power management system. The power supply 403 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.

The electronic device may further comprise an input unit 404, which input unit 404 may be used for receiving input digital or character information and generating keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.

Although not shown, the electronic device may further include a display unit or the like, which is not described herein. In particular, in this embodiment, the processor 401 in the electronic device loads executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 executes the application programs stored in the memory 402, so as to implement various functions as follows:

acquiring target medical detection data for a target physiological site of a subject to be diagnosed; performing focus identification on the target medical detection data to obtain focus attribute information corresponding to the target physiological part; acquiring a commonality analysis rule corresponding to focus attribute information of the target physiological part, wherein the commonality analysis rule indicates associated physiological information with commonality relation with the target physiological part; and carrying out disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result.

The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, embodiments of the present application provide a computer readable storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any of the medical diagnostic methods provided by embodiments of the present application. For example, the instructions may perform the steps of:

Wherein the computer-readable storage medium may comprise: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.

Because the instructions stored in the computer readable storage medium can execute the steps in any of the medical diagnosis methods provided by the embodiments of the present application, the beneficial effects that any of the medical diagnosis methods provided by the embodiments of the present application can be achieved, and detailed descriptions of the foregoing embodiments are omitted.

According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in various alternative implementations of the medical diagnostic aspects described above.

The foregoing has outlined a detailed description of a medical diagnostic method and related apparatus in accordance with the embodiments of the present application, wherein specific examples are presented herein to illustrate the principles and embodiments of the present application and to assist in understanding the methods and concepts of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, the present description should not be construed as limiting the present application.

Claims

1. A method of medical diagnosis, comprising:

2. The method of claim 1, wherein the associated physiological information comprises associated physiological site information having a commonality with the target physiological site;

the step of performing disease deduction analysis on the object to be diagnosed based on the associated physiological information and the focus attribute information to obtain a target diagnosis result comprises the following steps:

3. The method of claim 1, wherein the associated physiological information comprises associated physiological index information having a commonality with the target physiological site;

4. The method according to claim 1, wherein the method further comprises:

5. The method of claim 1, wherein the obtaining a commonality analysis rule corresponding to lesion attribute information of the target physiological site comprises:

6. The method according to claim 1, wherein the performing a disease deduction analysis on the object to be diagnosed based on the associated physiological information and the lesion attribute information to obtain a target diagnosis result includes:

7. The method of claim 6, wherein the method further comprises:

8. The method according to claim 1, wherein the performing focus recognition on the target medical detection data to obtain focus attribute information corresponding to the target physiological site includes:

performing focus recognition on the target medical detection data through the trained focus recognition model to obtain focus attribute information corresponding to the target physiological position;

before focus identification is carried out on the target medical detection data through the trained focus identification model to obtain focus attribute information corresponding to the target physiological position, the method further comprises:

9. A medical diagnostic apparatus, comprising:

10. An electronic device comprising a memory and a processor; the memory stores an application program, and the processor is configured to execute the application program in the memory to perform the operations in the medical diagnostic method according to any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor for performing the steps in the medical diagnostic method according to any of claims 1 to 8.

12. A computer program product comprising a computer program or instructions which, when executed by a processor, carries out the steps in the medical diagnostic method according to any one of claims 1 to 8.